1 code implementation • 11 Jul 2020 • Ke Sun, Brent Schlotfeldt, Stephen Chaves, Paul Martin, Gulshan Mandhyan, Vijay Kumar
In this work, we address the motion planning problem for autonomous vehicles through a new lattice planning approach, called Feedback Enhanced Lattice Planner (FELP).
Robotics
2 code implementations • 23 Oct 2019 • Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas
In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors.
no code implementations • 30 Sep 2019 • Alexander Robey, Arman Adibi, Brent Schlotfeldt, George J. Pappas, Hamed Hassani
Given this distributed setting, we develop Constraint-Distributed Continuous Greedy (CDCG), a message passing algorithm that converges to the tight $(1-1/e)$ approximation factor of the optimum global solution using only local computation and communication.
no code implementations • 26 Mar 2018 • Brent Schlotfeldt, Vasileios Tzoumas, Dinesh Thakur, George J. Pappas
In this paper, we provide the first algorithm, enabling the following capabilities: minimal communication, i. e., the algorithm is executed by the robots based only on minimal communication between them; system-wide resiliency, i. e., the algorithm is valid for any number of denial-of-service attacks and failures; and provable approximation performance, i. e., the algorithm ensures for all monotone (and not necessarily submodular) objective functions a solution that is finitely close to the optimal.